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/*
* Copyright Nick Thompson, 2023
* Use, modification and distribution are subject to the
* Boost Software License, Version 1.0. (See accompanying file
* LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
*/
#include <iostream>
#include <boost/math/optimization/differential_evolution.hpp>
using boost::math::optimization::differential_evolution_parameters;
using boost::math::optimization::differential_evolution;
double rosenbrock(std::vector<double> const & x) {
double result = 0;
for (size_t i = 0; i < x.size() - 1; ++i) {
double tmp = x[i+1] - x[i]*x[i];
result += 100*tmp*tmp + (1-x[i])*(1-x[i]);
}
return result;
}
int main() {
auto de_params = differential_evolution_parameters<std::vector<double>>();
constexpr const size_t dimension = 10;
// Search on [0, 2]^dimension:
de_params.lower_bounds.resize(dimension, 0);
de_params.upper_bounds.resize(dimension, 2);
// This is a challenging function, increase the max generations 10x from default so we don't terminate prematurely:
de_params.max_generations *= 10;
std::random_device rd;
std::mt19937_64 rng(rd());
// The global minima is exactly zero-but some leeway is required:
double value_to_reach = 1e-5;
auto local_minima = differential_evolution(rosenbrock, de_params, rng, value_to_reach);
std::cout << "Minima: {";
for (auto l : local_minima) {
std::cout << l << ", ";
}
std::cout << "}\n";
std::cout << "Value of cost function at minima: " << rosenbrock(local_minima) << "\n";
}
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